A Resources-Efficient Configurable Accelerator for Deep Convolutional Neural Networks

Abstract

Deep convolutional neural networks (DCNNs) have become one of the most popular approaches to many visual processing tasks. The majority of existing works on the accelerating DCNNs focus on high performance while neglecting the hardware resource utilization, like on-chip memory and DSP. In this paper, we propose a resources-efficient and configurable DCNN accelerator. A four-level processing-element (PE)-array-based structure is presented to realize high parallelism calculation of convolutional operation, and a new storage pattern named hybrid stationary (HS) is proposed to take full advantage of the used on-chip memory footprint and limited off-chip memory bandwidth. Moreover, roofline model is adopted to explore the design space of the given hardware resources. The proposed architecture achieves 113 G-ops/s at 100 MHz and consumes 784 DSP48 modules and 211.5 Block RAM modules on ZYNQ-7 ZC706 evaluation board. To the best of our knowledge, the proposed accelerator is the only implemented system on FGPA platform that can achieve multiple advantages: high-performanced, configurable, and efficient in power and resources utilization. It shows significant utilization improvement compared with the other available architectures.